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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurosci. Author manuscript; available in PMC Oct 20, 2011.
Published in final edited form as:
PMCID: PMC3083851
NIHMSID: NIHMS289895
GABAA inhibition controls response gain in visual cortex
Steffen Katzner,1,2,3+ Laura Busse,2 and Matteo Carandini1,2
1UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
2Smith-Kettlewell Eye Research Institute, San Francisco, CA 94115, USA
3Centre for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany
+Corresponding author: Steffen Katzner Centre for Integrative Neuroscience University of Tübingen Paul-Ehrlich-Str. 17 72076 Tübingen Germany ; steffen.katzner/at/uni-tuebingen.de
GABAA inhibition is thought to play multiple roles in sensory cortex, such as controlling responsiveness and sensitivity, sharpening selectivity, and mediating competitive interactions. To test these proposals we recorded in cat primary visual cortex (V1) following local iontophoresis of gabazine, the selective GABAA antagonist. Gabazine increased responsiveness by as much as 300%. It slightly decreased selectivity for stimulus orientation and direction, often by raising responses to all orientations. Strikingly, gabazine affected neither contrast sensitivity nor cross-orientation suppression, the competition seen when stimuli of different orientation are superimposed. These results were captured by a simple model where GABAA inhibition has the same selectivity as excitation, and keeps responses to unwanted stimuli below threshold. We conclude that GABAA inhibition in V1 helps enhance stimulus selectivity but is not responsible for competition among superimposed stimuli. It controls the sensitivity of V1 neurons by adjusting their response gain, without affecting their input gain.
Keywords: V1, GABAA inhibition, gain control, contrast saturation, cross-orientation suppression, orientation tuning, direction tuning
Information processing in sensory cortex relies on interactions between excitatory and inhibitory circuits, mediated by a diversity of GABAergic interneurons (Ascoli et al., 2008; Markram et al., 2004). As the specific functions of these interneurons are investigated, however, a broader question remains unanswered: In what way does GABA inhibition shape the output of excitatory neurons?
In primary visual cortex (V1), GABA inhibition has been proposed to play a fundamental role in establishing selectivity for stimulus orientation and direction of motion (Rose and Blakemore, 1974; Sillito, 1979; Tsumoto et al., 1979). According to this view, the sharpness of a neuron's orientation tuning curve would depend critically on inhibition suppressing responses at the flanks of the curve (Ferster and Miller, 2000; Sompolinsky and Shapley, 1997; Vidyasagar et al., 1996).
Inhibition in V1 has also been proposed to control responsiveness and to mediate competition between stimuli. The responsiveness of V1 neurons decreases when the contrast of an optimal stimulus increases (contrast saturation), or when an orthogonal stimulus is superimposed (cross-orientation suppression). These phenomena are collectively known as contrast gain control, or normalization (Busse et al., 2009; Heeger, 1992). They have been ascribed to GABA inhibition from other cortical neurons (Bonds, 1989; Carandini and Heeger, 1994; Carandini et al., 1997; Morrone et al., 1982; Somers et al., 1998).
These proposals were initially supported by experiments that sought to block GABAA inhibition with bicuculline. Bicuculline was seen to reduce selectivity for orientation and direction, abolishing selectivity entirely in some cells (Pettigrew and Daniels, 1973; Rose and Blakemore, 1974; Sillito, 1975; Sillito, 1979; Sillito et al., 1980; Tsumoto et al., 1979). Moreover, bicuculline was reported to abolish cross-orientation suppression (Morrone et al., 1987).
The intervening years, however, brought results that question the interpretation of these phenomena. Intracellular measurements indicate that inhibitory currents are as tuned for orientation as excitatory currents (Anderson et al., 2000; Marino et al., 2005; Monier et al., 2003), putting in doubt their involvement in establishing selectivity for orientation (Ferster and Miller, 2000). Indeed, orientation selectivity appears to be immune to intracellular blockade of inhibitory currents (Nelson et al., 1994). Moreover, recent studies that used bicuculline have generally found much smaller effects than the earlier studies (Li et al., 2008; Ozeki et al., 2004; Sato et al., 1996). Finally, numerous properties of cross-orientation suppression make it unlikely to arise from intracortical inhibition (Freeman et al., 2002; Li et al., 2006; Priebe and Ferster, 2006).
Possible reasons for the disagreement across studies are that bicuculline has substantial side effects, and that its impact on a network may depend critically on what portion of the cells it affects. Bicuculline has unwanted effects on calcium-dependent potassium channels (Kurt et al., 2006), so its use “should be discontinued in the study of inhibition” (Debarbieux et al., 1998).
A much more selective antagonist of GABAA receptors is SR95531, or gabazine (Kurt et al., 2006). Using local iontophoretic administration of gabazine, we examined the role of GABAA inhibition in determining neuronal responsiveness and selectivity in V1.
Experiments were carried out at the Smith-Kettlewell Eye Research Institute. All procedures were approved by the Institutional Animal Care and Use Committee.
Surgical protocol
Eight adult female cats were initially anesthetized with Ketamine (22 mg/kg) and Xylazine (1.1 mg/kg). Following venous cannulation and tracheotomy, anesthesia was maintained for the subsequent 60–85 hours with Fentanyl (typically 10 μg/kg/hr), supplemented by inhalation of N2O mixed with O2 (typically in a ratio of 70:30), and, as needed, with Sodium Pentothal (up to 2 mg/kg/hr). A craniotomy was performed over area V1. The pupils were dilated with atropine. Nictitating membranes were retracted with phenylephrine, and the eyes were protected with gas-permeable contact lenses. Muscle relaxation was induced with Pancuronium Bromide (0.15 mg/kg/hr). The animals were artificially respirated and received an antibiotic (Cephazolin, 20 mg/kg, twice a day), an anti-cholinergic agent (Atropine sulfate, 0.05 mg/kg, daily), and an anti-inflammatory steroid (Dexamethasone, 0.4 mg/kg, daily). Fluid balance was maintained by intravenous infusion. Body temperature was maintained near 37°C with a heating pad. Depth of anesthesia was assessed from the EEG, the heart rate, and the level of expired CO2 concentration.
Recordings and microiontophoresis
Microiontophoresis was performed with a NeuroPhore BH-2 system (Harvard Apparatus) via 3-barrel carbon fiber combination electrodes (Carbostar-4, Kation Scientific). Micropipette barrels were filled with gabazine (SR95531, 10 mM, ph 3.0, Tocris Bioscience, dissolved in 0.9% NaCl). Ejection currents ranged from 50 nA to 150 nA, and retention current was −30 nA. Extracellular activity measured with the carbon fiber electrode was amplified, band-pass filtered between 300 Hz and 8 kHz, and digitized at 12 kHz. Estimates of the recording depth were based on micromanipulator readings.
In some experiments (10/19 data sets), we implanted a 10×10 microelectrode array with an electrode length of 1.5 mm and a grid spacing of 400 μm (Blackrock Microsystems). Methods for these recordings are provided elsewhere (Nauhaus and Ringach, 2007). Spikes were extracted by bandpass filtering between 250 Hz and 7.5 kHz. Signals that crossed an appropriate threshold were saved to disk and defined as multi-unit activity. The lateral distance between the carbon fiber electrode and the proximal edge of the array was typically 0.5–1.5 mm. The exact distance between electrode and array depends on relative depth, which we did not control.
Experimental design
Stimuli were generated with the Psychophysics Toolbox (Kleiner et al., 2007) and presented monocularly on a calibrated CRT monitor positioned 57 cm in front of the animal's eyes (Sony Trinitron 500PS, mean luminance 32 cd/m2, refresh rate 120 Hz). Stimuli lasted 2 or 3 s and were separated by a uniform gray field (~4 s intervals). To measure responses, we averaged firing rate across the entire stimulus duration.
When a single neuron was isolated, we used drifting gratings to characterize its preferences for orientation, direction, spatial frequency, temporal frequency, and size. We then performed pre-drug control measurements of the following properties: (1) Orientation tuning: drifting sinusoidal gratings at 50% contrast varying in orientation, with optimal spatial frequency (0.1–1.7 cycles/deg) and optimal temporal frequency (1–4 Hz); (2) Contrast saturation: drifting sinusoidal gratings varying in contrast, presented at the optimal orientation, spatial frequency, and temporal frequency; (3) Cross-orientation suppression: drifting plaids obtained by summing an optimally oriented test grating and an orthogonal mask grating. The contrast of the mask was 25%. Stimulus diameters varied from 2.0–12.8 deg except for 5 experiments, in which we presented full-field gratings. Each of these measurements consisted of 4 to 10 consecutive blocks of stimulus presentations. Within each block, 5–16 stimuli were presented in random order.
We then applied gabazine at increasing levels of ejection current, while repeatedly measuring neuronal responses. Care was taken not to induce bursting (epileptiform) activity. When neuronal responses were significantly elevated, drug application was terminated. Retention current was applied again and further measurements were taken until recovery was achieved (usually within 10–45 min). Sites where spikes could not be held long enough or where responses failed to recover were excluded from further analysis (26 out of 45). The effects of iontophoresis were not due to the current injection per se: responsiveness either kept increasing or remained significantly elevated in the minutes that followed the end of gabazine ejection.
Descriptive functions
Contrast responses were fitted with the hyperbolic ratio function (Albrecht and Hamilton, 1982):
equation M1
(1)
where c is stimulus contrast. The function has four parameters: baseline response R0, responsiveness Rmax, semisaturation contrast c50, and exponent n. The best fit model parameters were determined by simultaneously fitting the responses in the control and gabazine conditions. To limit the number of free parameters, we took the exponent n to be the same under both conditions. The semisaturation contrast c50 was allowed to vary across the two conditions, or was forced to be the same across the two conditions (“response gain model”).
To study cross-orientation suppression we varied the test contrast while presenting a mask (25% contrast) and measured two additional contrast responses: one in the control condition and one in the gabazine condition. Data from these conditions were also fitted with a hyperbolic ratio (Equation 1). For simplicity, we fitted the responses without mask with the “response gain model”; based on earlier work (Freeman et al., 2002) we allowed a mask to affect only the baseline activity R0 and the semisaturation contrast c50.
We fitted the orientation tuning of responses with a sum of two Gaussians with peaks 180° apart (Carandini and Ferster, 2000):
equation M2
(2)
In this expression, θ is stimulus orientation (0° to 360°). The function has five parameters: preferred orientation θp, tuning width σ, baseline response R0, response at the preferred orientation Rp, and response at the null orientation Rn. We took the preferred orientation θp to be the same across the control and gabazine conditions.
Simulations based on intracellular data
We considered published measurements of excitatory conductance, inhibitory conductance, membrane potential, and firing rate (Anderson et al., 2000), and simulated the effects of gabazine in those experiments. We chose four cells that were tested in the full 360-degree range of orientations and directions and that satisfied a criterion for quality of the estimates of excitation and inhibition (namely, that a linear model of the effect of injected current predicted >75% of the variance of the membrane potential).
Model of orientation selectivity
When modeling the effects of gabazine on orientation selectivity, we consider firing rate R to be a rectified version of membrane potential V (Carandini and Ferster, 2000).
equation M3
(3)
We take the membrane potential to be the difference between currents due to excitation and inhibition:
equation M4
(4)
For simplicity, in these expressions we take membrane potential and synaptic currents to be all in units of spikes/s, and we set spike threshold to zero. Further, we assume that the effect of gabazine is to abolish inhibition (VI (θ) = 0 under gabazine).
We describe the orientation tuning of excitation and inhibition with a sum of two Gaussians peaking 180°apart (Anderson et al., 2000):
equation M5
(5)
Here, the subscript x can take the values E for excitation and I for inhibition. The shape of this function is determined by three parameters: preferred orientation θp, tuning width σx, and direction term d. The latter varies between 0 and 1 and is related to the direction index D as d = (1 − D)/(1 + D). The remaining parameters ax and bx are a scaling factor that determines the amplitude of the modulation caused by orientation, and a baseline value.
The optimal parameters were determined by fitting simultaneously responses in control and gabazine conditions. For simplicity, we imposed that the preferred orientation θp and direction term d were the same for excitation and inhibition. For excitation, we allowed all remaining parameters to be free. For inhibition, we considered three possibilities: (1) “free inhibition”, where the tuning width of inhibition σI is allowed to differ from that for excitation σI; (2) “matching inhibition”, where the two are imposed to be the same (σI = σE); (3) “untuned inhibition”, where inhibition is imposed to be constant (VI(θ) = bI).
We evaluated model performance with bootstrap hypothesis tests (Efron and Tibshirani, 1993). The application of this analysis to neural responses is detailed elsewhere (Carandini et al., 1997). Briefly, for each model we tested the null hypothesis that the means of the probability distributions underlying the responses were identical to the predictions of the model. We determined an observed prediction error, and computed the probability of observing at least as large an error if the null hypothesis were true. To obtain data that conformed to the null hypothesis, we transformed our measurements by equating their means across trials to the model predictions. We then obtained 1000 bootstrap samples from this transformed data set and computed the prediction error for each of them. The proportion of bootstrap samples for which the prediction error was larger than the observed prediction error is the achieved significance level (ASL). The smaller the ASL, the stronger is the evidence against the null hypothesis, i.e. against the model.
We recorded from 45 cells in cat V1. We report here on 19 of these cells, which were held long enough to measure their responses before, during, and after gabazine iontophoresis, with the last measurements displaying a full recovery from gabazine. These 19 cells were identified as complex and were mostly located in superficial layers.
As might be expected, the most visible effect of gabazine on V1 responses was to increase firing rates. We illustrate this effect on an example neuron (Figure 1).The neuron had a low resting firing rate (Figure 1A), responded markedly more to a grating of preferred orientation (Figure 1B) than to an orthogonal grating (Figure 1C), and showed cross-orientation suppression when the two gratings were superimposed (Figure 1D). Applying gabazine increased the firing rate of this neuron both at rest (Figure 1E) and in response to the visual stimuli (Figure 1F–H). Gabazine made the neuron respond more to some stimuli that were previously ineffective (Figure 1G) but it did not abolish selectivity (Figure 1F,G) and it did not abolish cross-orientation suppression (Figure 1H). The effects of gabazine were reversible: 30 min after switching off the iontophoresis, the responses returned to the level observed in the control condition (Figure 1I–L).
Figure 1
Figure 1
Examples of effects of the GABAA antagonist gabazine on spike responses. A–D: Responses of an example neuron (88.3.12-14-18) to a blank screen (A), to a grating drifting in the preferred (B) and orthogonal direction (C), and to the plaid obtained (more ...)
Other aspects of the responses were not changed. In particular, the variability of the responses across trials remained largely constant: the ratio of variance to mean measured in 500 ms windows was 1.55 ± 0.06 in the control measurements vs. 1.52 ± 0.04 under gabazine (SE, n = 19 neurons, p = 0.49). Similarly, the degree to which neurons acted as simple or complex cells remained largely unaffected: the ratio of F1 to DC response component (Skottun et al., 1991) was 0.31 ± 0.04 in control vs. 0.36 ± 0.06 under gabazine (an insignificant change, p = 0.3).
Role of GABAA inhibition in contrast sensitivity
We first asked how GABAA inhibition affects the input-output properties of V1 neurons, i.e. the dependence of their responses on stimulus contrast. GABAA inhibition could shape a neuron's responsiveness by controlling the gain at the input, at the output, or at both places.
Pure changes in input gain or in output gain would be particularly simple to describe. If GABAA inhibition scaled the input it would effectively multiply the stimulus contrast that reaches the neuron, whereas if it scaled the output it would multiply responses by the same factor at all contrasts. In the first case, blocking GABAA synapses with gabazine would shift contrast responses to the left in logarithmic contrast, i.e. change the semisaturation contrast of the neurons. In the second case it would scale the curves vertically.
We measured the effects of gabazine on contrast responses, and found strong evidence in favor of the second scenario: GABAA inhibition controls response gain but not contrast gain. We illustrate this evidence on four example neurons (Figure 2A–D). Gabazine substantially increased the maximum response in all four neurons, by factors of 3.3, 2.6, 5.3 and 4.0. In two of the neurons (Figure 2A,D) it also markedly increased the baseline response. Gabazine, however, did not affect contrast sensitivity: the contrast required to produce half of the cells' saturating response was the same under control and gabazine conditions (Figure 2A–D, vertical lines).
Figure 2
Figure 2
Effects of gabazine on contrast responses. A–D: Contrast response functions of four example cells, measured in control condition (white), under gabazine (black), and in recovery condition (gray). Curves are fits of a response gain model, which (more ...)
Indeed, the data from all neurons could be fitted with a simple “response gain” model, in which gabazine does not affect the contrast gain of the neurons. The model involves two contrast response functions (Equation 1) having the same semisaturation contrast c50 for the control condition and for the gabazine condition. The model provided good fits, as can be judged on the example neurons (Figure 2A–D). It explained a high proportion of the variance (98.7 ± 0.9%, SD, n = 15) and could not be rejected in any neuron based on an achieved significance level (ASL) of p < 0.05 (Figure 2H, abscissa). The parameters of the model can thus be trusted; they indicate that in practically all neurons gabazine increased both baseline response (Figure 2E) and maximum response (Figure 2F). Maximum responses, in particular, increased on average by 167 ± 25% (SE, n = 15). This increase was significant in all cells (p < 0.001).
An alternative model, in which gabazine affected only the contrast gain of the neuron, provided markedly inferior fits (Figure 2H). We considered a pure contrast gain model, which allowed gabazine to affect the semisaturation contrast and the baseline response of the neuron, but not its maximal response. Compared to the response gain model, the contrast gain model explained a markedly smaller proportion of the variance (94.6 ± 2.8%, SD, p = 0.0001) and could be rejected for 6 out of 15 neurons based on an ASL of p ≤ 0.05 (Figure 2H, ordinate).
Finally, a more general model in which gabazine could affect both response gain and contrast gain did not improve on the excellent performance of the response gain model. The more general model allowed for changes not only in baseline and maximal response, but also in semisaturation contrast. This additional parameter, however, provided little advantage over the response gain model: the more general model explained a similar proportion of the variance (99.0% ± 0.9%, SD, n = 15) as the response gain model, with extremely similar values for the ASL. Critically, the values of semisaturation contrast measured by the more general model under gabazine were indistinguishable from those measured in the control condition (Figure 2G). These results, therefore, further indicate that GABAA inhibition in V1 is a powerful mechanism for the control of response gain, but is not involved in the control of contrast gain, i.e. of input gain.
Role of GABAA inhibition in cross-orientation suppression
We next considered another well-documented signature of contrast gain control or normalization: cross-orientation suppression (Allison et al., 2001; Bauman and Bonds, 1991; Bonds, 1989; Busse et al., 2009; Carandini et al., 1997; DeAngelis et al., 1992; Morrone et al., 1982; Sengpiel et al., 1998; Sengpiel and Blakemore, 1994). Suppression can be observed by superimposing two gratings: one (the test) with the preferred orientation, and the other (the mask) with a different orientation (Figure 1B–C). The response to the superposition of the stimuli is typically lower than expected from summation (Figure 1D). The strength of this effect depends on the relative contrasts of test and mask (Figure 3A,B). When the test is presented alone, V1 responses simply increase with increasing test contrast (Figure 3B, filled circles). Adding a mask to the test reduces these responses, effectively shifting the contrast-response functions to the right (Figure 3B, open circles). Cross-orientation suppression is present with masks of all orientations (DeAngelis et al., 1992); we chose orthogonal masks because they provide the least drive to the neurons, so their effect is almost purely suppressive.
Figure 3
Figure 3
Cross-orientation suppression. A: Stimuli were test gratings at the cell's preferred orientation (first row), mask gratings at the orthogonal orientation (left column), or plaids obtained by summing the two (remaining panels). B: Contrast response functions (more ...)
To assess whether cross-orientation suppression relies on GABAA inhibition, we asked whether its strength is affected by gabazine (Figure 3C–J). We illustrate the results for two example cells (Figure 3C–F). Both cells showed strong cross-orientation suppression: adding a mask to the test grating increased the semisaturation contrast c50 from 10% to 49% in one cell (Figure 3C), and from 40% to 100% (the upper limit in our fits) in the other cell (Figure 3E). If we define a suppression index as the ratio of semisaturation contrasts measured with and without the mask (Freeman et al., 2002), these two cells showed suppression indices of 5.1 (the largest in our sample) and 2.5. Remarkably, this strong degree of suppression was completely unaffected by blocking GABAA inhibition with gabazine (Figure 3D,F). Gabazine markedly increased the overall firing rates – as expected – but did not affect the semisaturation contrast c50 either in the test alone case (as seen in the previous section) or in the test plus mask case. Indeed, under gabazine the suppression indices were 4.9 for the first cell (Figure 3D) and 2.5 for the second cell (Figure 3F), which are essentially the same values as in the control condition.
Very similar results were obtained across the population of recorded cells: gabazine did not reduce cross-orientation suppression. Adding a mask to the test grating suppressed responses for most of the cells not only in the control condition (Figure 3G), but also under gabazine (Figure 3H). Gabazine did not decrease the suppression index; if anything, it increased it (Figure 3I).
Indeed, the data were well fit by a model where the only effect of gabazine was to increase response gain and baseline response. In this model there are two semisaturation contrasts: one measured without the mask and one measured with the mask, and the values of these semisaturation contrasts are the same in the control case and under gabazine. This model provided excellent fits to the data. (Figure 3J), explaining on average 98% ± 1% of the variance (SD, n =15). Cross-orientation suppression, therefore, is not due to GABAA inhibition.
Role of GABAA inhibition in stimulus selectivity
Having identified a specific role for GABAA inhibition in response gain control, we now ask whether this effect is the same for all stimulus orientations and directions. In other words, we asked whether GABAA inhibition also plays a role in shaping a neuron's selectivity for these attributes. To this end, we measured tuning curves for stimulus orientation and direction of motion. We illustrate the results for four example cells (Figure 4A–D).
Figure 4
Figure 4
Effects of gabazine on orientation tuning. A–D: Four example cells. Symbols indicate whether tuning curves were measured before, during or after gabazine iontophoresis. Curves are fits of a descriptive tuning function (sum of Gaussians). Stimulus (more ...)
Similar to the previous experiments (Figure 2F), we found that the main effect of gabazine was invariably an overall increase in responsiveness. Responsiveness (measured here as the average response across orientations) increased by at least a factor of 2.5 in these four cells, and in a dramatic example by almost a factor of 4 (Figure 4B). Across the sample, gabazine increased responsiveness by 242 ± 48% (SE, n = 19, Figure 4E). The increase was significant in all cells (p < 0.002, bootstrap test).
Additional effects were a broadening of tuning width and a reduction in direction selectivity. These effects were strongest in the cells that were initially sharply tuned for orientation (e.g. Figure 4D), or that displayed marked direction selectivity (e.g. Figure 4B,D).
To assess the effects of gabazine on overall orientation selectivity (Figure 4F) we used a global measure of selectivity, the circular variance (Ringach et al., 2002). Circular variance grows with the mean of the response across orientations (the untuned response) and decreases with the amplitude of the modulation seen by changing orientation (the tuned response). Lower values of circular variance, therefore, indicate stronger selectivity. Gabazine affected circular variance significantly in 11 of 19 cells (p < 0.05, bootstrap test, Figure 4F, open symbols). In these cells, circular variance increased by 52 ± 15% (SE, n = 19). Gabazine, therefore, tended to increase the untuned responses more than the tuned responses.
In neurons that were particularly selective for orientation, gabazine broadened the orientation tuning curves (Figure 4G). Tuning width (the parameter σ in the Gaussian curve fitted to the data) changed significantly under gabazine in 7 of 19 cells (p < 0.05, bootstrap test, Figure 4G). In these cells, it increased by 95 ± 28% on average (SE, n = 19). Consistent with previous results obtained with bicuculline (Li et al., 2008), the cells where tuning width increased were among the most highly selective for orientation, with an average σ of 15 ± 1 deg vs. 23 ± 2 deg for the remaining cells.
Finally, gabazine significantly decreased direction selectivity, especially in those cells that showed significant selectivity to begin with (Figure 4H). We measured direction selectivity through a direction index (Carandini and Ferster, 2000). Gabazine reduced the direction index in 6 out of 19 cells (p < 0.05, bootstrap test, Figure 4H). In these cells, the direction index was reduced by 34 ± 7% (SE, n = 19). Similar to the changes in tuning width, the cells whose direction index was reduced tended to be the most direction selective (D = 0.67 ± 0.08 for those 6 cells, vs. 0.28 ± 0.05 for the remaining ones).
All of these effects were local to the region of iontophoresis, being completely absent in responses measured about one millimeter away (Figure 5). We compared the effects of gabazine at the site of iontophoresis (Figure 5A) with those measured ~0.5–1.5 mm away with a 10×10 array of electrodes (Figure 5B). Whereas the responses of the local sites strongly increased in the presence of gabazine (mean factor of 3.4 ± 0.5, SE, p < 0.0001, n = 19), the responses measured from distant sites were unaffected: the average responsiveness increased by an insignificant factor of 1.1 ± 0.1 (SE, n = 302 sites measured in 10 experiments, p = 0.6), with no changes in tuning curves (Figure 5B).
Figure 5
Figure 5
Effects of gabazine are local. A: Average of tuning curves measured at the iontophoresis site in 19 experiments, aligned relative to preferred orientation. B: Average of 302 tuning curves measured with a nearby multielectrode array in 10 of the 19 experiments. (more ...)
Insights from intracellular data
To gain some insight into these results, we asked whether they are consistent with what is known about excitation and inhibition from intracellular measurements (Figure 6). We considered published measurements of excitatory conductance, inhibitory conductance, membrane potential, and firing rate (Anderson et al., 2000), and simulated the effects of gabazine in those experiments.
Figure 6
Figure 6
Simulation of effects of gabazine based on actual measurements of excitation and inhibition. Shown are four neurons (columns) from a study employing intracellular measurements (Anderson et al. 2000). A: Firing rates measured (white symbols) and predicted (more ...)
We illustrate the results for four cells that were generally well tuned, with three out of four giving clear spike responses to gratings of the preferred orientation (Figure 6A, open symbols). The corresponding depolarizations in membrane potential (Figure 6B, gray shading) were accompanied by a concomitant increase in excitatory conductance (Figure 6C) and in inhibitory conductance (Figure 6D), which are typically similarly tuned for orientation (Anderson et al., 2000; Marino et al., 2005; Monier et al., 2003).
To simulate the effects of gabazine, we set inhibitory conductances to zero and computed the resulting membrane potentials taking into account the reduced conductance of the neuron; the result is a substantially depolarized potential (Figure 6B, blue shading). Having made a rough estimate of how membrane potentials are transformed into firing rates (Carandini and Ferster, 2000), we could then predict how gabazine would affect the tuning of firing rate (Figure 6A, closed symbols). The main predicted effect is a nonspecific increase in response and a broadening of tuning widths – similar effects to those obtained in our extracellular measurements (Figure 4).
Excitation, inhibition, and threshold
The exercise performed on intracellular data suggests that the effects of gabazine on the tuning curves for orientation can be explained by known properties of synaptic inhibition. Indeed, it indicates that the effects we have seen under gabazine would be fully expected if the inhibition removed by gabazine has the same tuning as excitation. A key role in this prediction is played by threshold, which normally prevents spiking responses to most orientations, but is no longer able to do so under gabazine.
To test these hypotheses quantitatively, we considered a very simple cellular model (Figure 7). In the model, the firing rate of the neurons (Figure 7A) depends on an underlying membrane potential response (Figure 7B), and the relation between the two (Figure 7B, shaded area) is described by a simple rectification function, a threshold followed by a linear relationship (Carandini and Ferster, 2000; Cardin et al., 2007; Priebe et al., 2004). The membrane potential response, in turn, is the difference of two sets of synaptic contributions: excitation and inhibition (Figure 7C,D). In agreement with intracellular measurements (e.g. Figure 6C,D), we take excitation and inhibition to have the same preferred orientation (Anderson et al., 2000; Marino et al., 2005; Monier et al., 2003), and direction (Priebe and Ferster, 2005). Finally, in the model gabazine abolishes inhibition while leaving excitation unchanged (Figure 7G,H), leading to more depolarized membrane potentials and consequently to firing rates that are higher and less tuned (Figure 7E,F). The model is specified by few free parameters, which determine the shapes and offsets of Gaussian tuning curves for excitation and inhibition.
Figure 7
Figure 7
A simple cellular model. A: Tuning of firing rate in control condition. B: Corresponding tuning of membrane potential responses. Dashed line indicates threshold. Shaded area indicates responses that elicit nonzero firing rates. C: Tuning of excitation. (more ...)
This highly simplified cellular model provided an excellent account of the effects of gabazine on the tuning curves (Figure 8). For the four example cells, the model captured how gabazine increases responsiveness and reduces selectivity for stimulus orientation and direction (Figure 8A–D, solid curves). Similar results were obtained in the remaining cells, with the model explaining on average 96% ± 2% (SD, n=19) of the variance in the responses. This good performance of the model was statistically significant in 15 out of 19 cells, where the model could not be rejected with an ASL of p < 0.05. Specifically, the model was able to capture the main effects of gabazine on responsiveness, circular variance, tuning width, and direction selectivity (Figure 8E–H). The exceptions (values off the identity line) generally involve the fits to the control responses (open symbols). These responses involve lower firing rates, which the fitting procedure tries less hard to capture.
Figure 8
Figure 8
Predictions and performance of the simple cellular model. A–D: responses of the example neurons of Figure 4, fitted with the simple cellular model (Figure 7), assuming matching inhibition (solid curves) or untuned inhibition (dashed curves). (more ...)
A simpler version of the model, in which inhibition is not tuned for orientation, proved to be inadequate. In this version (“untuned inhibition”), inhibition is specified by one parameter (offset) rather than two (offset and tuned amplitude). This version of the model provided worse fits (Figure 8A–D, dashed curves). On average it accounted for only 93% ± 4% (SD, n = 19) of the variance of the responses, which is significantly less than the version with matching inhibition (p < 0.0001, paired t-test). Throughout, the prediction errors of the untuned inhibition model were larger than those of the matching inhibition model (Figure 8M–P). Accordingly, the fits of the untuned inhibition version of the model provided noticeably worse scores of ASL, and this model could be rejected at the p < 0.05 level for 8 of the 19 cells.
Conversely, a more general version of the model, in which excitation and inhibition were not required to match, did not provide any advantages. In this version (“free inhibition model”) the tuning width of inhibition was free to be narrower or broader from that of excitation. This model includes as a special case the matching inhibition model, in which excitation and inhibition have identical tuning. Yet, the additional free parameter for the tuning of inhibition did not noticeably improve the fits: for each neuron the ASL score achieved under free inhibition was almost identical to the ASL score under matching inhibition. Moreover, in those four cases in which we had to reject the model with matching inhibition, we also had to reject the model with free inhibition.
These analyses confirm the impression provided by our initial simulations based on intracellular data. They indicate that the effects of gabazine can be most parsimoniously explained by a loss of an inhibitory drive that has the same tuning as the excitatory drive.
Using a combination of pharmacology, electrophysiology, and simple modeling, we have investigated the role of local GABAA inhibition in shaping the output of neurons in cat primary visual cortex. We have recorded from cells before, during and after iontophoresis of the highly selective GABAA antagonist gabazine. The results indicate that GABAA inhibition contributes in simple ways to the sensitivity and selectivity of V1 neurons.
Our first set of results concerns the sensitivity of neurons: we found that local GABAA inhibition controls the response gain but not the contrast gain of V1 neurons. These results contradict earlier proposals for a key role of GABAA inhibition in contrast gain control (Carandini and Heeger, 1994; Carandini et al., 1997; Somers et al., 1998), as they indicate that the contrast sensitivity of V1 neurons does not rely on intracortical inhibition. This conclusion may well extend to other cortical areas; for instance, contrast sensitivity in area MT of primates is unaffected by blocking inhibition with bicuculline (Thiele et al., 2004).
These results suggest that GABAA inhibition may be a natural substrate for the control of responsiveness seen during the deployment of visual attention. A recent study, indeed, argues that attention changes the response gain but not the contrast gain of V1 neurons (Lee and Maunsell, 2010). Attention could achieve this goal rather simply if it acted on the strength of GABAA inhibition.
Our finding that cross-orientation suppression is unaffected by gabazine is direct evidence that this form of suppression does not rely on intracortical inhibition. This finding corroborates the interpretation of previous studies that provided indirect evidence against a role of inhibition in cross-orientation suppression (Carandini et al., 2002; Freeman et al., 2002; Li et al., 2006; Priebe and Ferster, 2006). In fact, the only data supporting a GABAergic origin of cross-orientation suppression were obtained by blocking inhibition with a diffuse application of bicuculline (Morrone et al., 1987). The interpretation of those data is made difficult by the limitations of bicuculline (Debarbieux et al., 1998).
Contrast saturation and cross-orientation suppression are key pieces of evidence for divisive normalization in V1 (Busse et al., 2009; Carandini et al., 1997; Heeger, 1992). Our results show that they don't rely on GABAA inhibition, and thereby falsify an early proposal for the biophysical substrate of normalization (Carandini and Heeger, 1994; Carandini et al., 1997). Normalization is now thought to rely on alternative mechanisms, at least for phenomena that arise within the receptive field (Carandini et al., 2002; Finn et al., 2007; Priebe and Ferster, 2006). It is currently unclear whether normalization relies on GABAA inhibition for suppression originating in the surrounding regions (Haider et al., 2010; Ozeki et al., 2009).
Our second set of results concerns stimulus selectivity: we found that local GABAA inhibition contributes to the selectivity of V1 neurons for stimulus orientation and direction. Inhibition contributes to selectivity simply by matching the selectivity of excitation, thereby keeping the responses to most stimuli below threshold.
The effects of gabazine on tuning curves – increased firing rates, broadened tuning width, and reduced direction selectivity – confirm some but not all of the previous results obtained with bicuculline. Earlier studies reported very strong effects: bicuculline was seen to broaden markedly the selectivity for orientation or direction of most neurons, in some cells abolishing it altogether (Eysel and Shevelev, 1994; Pettigrew and Daniels, 1973; Rose and Blakemore, 1974; Sillito, 1975; Sillito, 1979; Sillito et al., 1980; Tsumoto et al., 1979). More recent studies, however, reported subtler effects that are more similar to the ones we observed (Li et al., 2008; Ozeki et al., 2004; Sato et al., 1996). As with gabazine, the size of the effects seen with bicuculline depended on the degree to which neurons are selective in the first place (Li et al., 2008). Overall, the large variability of effects reported in bicuculline studies might be related to the inferior selectivity and non-GABAergic side effects of this drug (Debarbieux et al., 1998; Kurt et al., 2006; Wermuth and Bizière, 1986), or to differences in doses across studies.
To capture the effects of gabazine on orientation tuning curves we considered a simple cellular model based on a precise match between the tuning of inhibition and excitation. Excitation and inhibition in visual cortex have similar selectivity for orientation (Anderson et al., 2000; Marino et al., 2005; Monier et al., 2003) though perhaps not for direction (Monier et al., 2003; Priebe and Ferster, 2005) or for stimulus size (Haider et al., 2010; Ozeki et al., 2009). The selectivity of inhibition matches that of excitation also in somatosensory cortex (Okun and Lampl, 2008) and in auditory cortex (Wehr and Zador, 2003). Inhibition and excitation, moreover, are matched not only in sensory-evoked activity, but also in the ongoing activity (Adesnik and Scanziani, 2010; Haider et al., 2006; Okun and Lampl, 2008). This match between excitation and inhibition, therefore, may be a fundamental organizing principle of neocortex (but not necessarily all of cortex, see Poo and Isaacson, 2009).
The cellular model that we considered incorporates highly simplified assumptions. First, it assumes that membrane potential is the result of the subtraction of inhibition from excitation. Subtraction is a simplification because inhibition does provide conductance increases at least at some orientations (Anderson et al., 2000; Borg-Graham et al., 1998), which would result in a non-linear integration of synaptic inputs (a fact that we did include in our simulations based on intracellular data). Second, the model assumes that the relationship between firing rate and the underlying membrane potential is simply a threshold followed by a linear dependence. Intracellular measurements support this notion, but also indicate that noise in the membrane potential (which we ignored) smoothes the threshold and turns it into an exponent (Carandini, 2004; Carandini and Ferster, 2000; Priebe and Ferster, 2008). Third, we assumed that the responses observed under gabazine mostly reflect excitatory synaptic inputs, as synaptic inhibition has been largely abolished. This is oversimplified, as gabazine is likely to have reached the perisomatic region more than the distal dendrites (see below). Also, though GABAB receptors are unlikely to shape transient visual responses, they may contribute to responses on the scale of seconds. Finally, we assumed that gabazine affected only inhibition, whereas it is likely to have increased (and slightly broadened in tuning) the excitation that the neurons receive from the local network.
Even though it was so highly simplified, the model provided a full account of the effects of gabazine on orientation tuning. It predicted the effects of gabazine on overall responsiveness, tuning width, and direction selectivity. This success suggests that all the effects of gabazine – including those that had earlier been ascribed to broadly tuned inhibition – can be explained by inhibition having the same tuning as excitation.
One limitation of our study is that it addresses mostly the inhibition that neurons receive near the soma. Our electrode likely recorded signals near the soma, and given that the ejecting pipette was 20 μm away, the concentration of gabazine is likely to have been largest in the perisomatic region. Concentration at the distal dendrites may have been low, given that gabazine did not affect electrode arrays implanted 0.5–1.5 mm away from the ejecting pipette.
The conclusions that can be drawn from this study, therefore, concern mostly perisomatic inhibition, rather than inhibition on distal dendrites. Indeed, this limitation that is shared with many other studies on the role of inhibition. Intracellular studies of the orientation tuning of excitation and inhibition, for instance, are largely based on currents injection at the soma. These currents do not fully spread to distal dendrites (Williams and Mitchell, 2008), so the conductances that are estimated are largely perisomatic.
Another limitation of our study concerns the specificity of our manipulations. While we strived to keep the injection of gabazine local, the drug is likely to have spread beyond the neuron under study, potentially altering the function of local microcircuits. Substances applied iontophoretically with similar methods can diffuse as much as 200–600 μm (Candy et al., 1974). Pyramidal neurons in sensory cortex excite one another over comparable distances (Thomson and Lamy, 2007). Therefore, the increase in responsiveness seen with gabazine might be due not only to cellular effects – the blockade of perisomatic GABAA receptors – but also to network effects: an increase in mutual excitation brought about by increased responsiveness in interconnected neurons. We can't distinguish these two effects, and neither can all previous studies that attempted to block inhibition, with the exception of one that blocked inhibition intracellularly (Nelson et al., 1994). The good agreement between our results and those of that study indicates that the main effects that we observed are cellular.
In conclusion, we found that local inhibition mediated by GABAA receptors controls tuning width and direction selectivity not by being broadly tuned, but rather by working hand in hand with an excitatory drive that has the same selectivity. Together, excitation and inhibition exploit the ability of the spike threshold to sharpen orientation tuning, improve direction selectivity, and set the appropriate responsiveness. GABAA inhibition plays a substantial role in the control of responsiveness: it makes neurons more or less responsive to stimuli, without affecting their sensitivity to contrast. In other words, GABAA inhibition does not participate in input gain control, but rather is a determinant factor in response gain control.
Acknowledgements
We thank Andrea Benucci and Andrew Zaharia for help with experiments and analyses, and James Cottam, Massimo Scanziani, and Adam Sillito for advice on the manuscript. This work was supported by National Institutes of Health grant EY-17396 and by an Advanced Investigator Award (CORTEX) from the European Research Council. L.B. was supported by a postdoctoral fellowship of the German Academy of Sciences Leopoldina (BMBF-LPD9901/8-165). M.C. holds the GlaxoSmithKline / Fight for Sight Chair in Visual Neuroscience.
  • Adesnik H, Scanziani M. Lateral competition for cortical space by layer-specific horizontal circuits. Nature. 2010;464:1155–1160. [PMC free article] [PubMed]
  • Albrecht DG, Hamilton DB. Striate cortex of monkey and cat: contrast response function. J Neurophysiol. 1982;48:217–237. [PubMed]
  • Allison JD, Smith KR, Bonds AB. Temporal-frequency tuning of cross-orientation suppression in the cat striate cortex. Visual Neuroscience. 2001;18:941–948. [PubMed]
  • Anderson JS, Carandini M, Ferster D. Orientation Tuning of Input Conductance, Excitation, and Inhibition in Cat Primary Visual Cortex. J Neurophysiol. 2000;84:909–926. [PubMed]
  • Ascoli GA, Alonso-Nanclares L, Anderson SA, Barrionuevo G, Benavides-Piccione R, Burkhalter A, Buzsáki G, Cauli B, DeFelipe J, Fairén A, et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat Rev Neurosci. 2008;9:557–568. [PMC free article] [PubMed]
  • Bauman LA, Bonds AB. Inhibitory refinement of spatial frequency selectivity in single cells of the cat striate cortex. Vision Res. 1991;31:933–944. [PubMed]
  • Bonds AB. Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex. Vis Neurosci. 1989;2:41–55. [PubMed]
  • Borg-Graham LJ, Monier C, Frégnac Y. Visual input evokes transient and strong shunting inhibition in visual cortical neurons. Nature. 1998;393:369–373. [PubMed]
  • Busse L, Wade AR, Carandini M. Representation of Concurrent Stimuli by Population Activity in Visual Cortex. Neuron. 2009;64:931–942. [PMC free article] [PubMed]
  • Candy JM, Boakes RJ, Key BJ, Worton E. Correlation of the release of amines and antagonists with their effects. Neuropharmacology. 1974;13:423–430. [PubMed]
  • Carandini M. Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex. PLoS Biol. 2004;2:1483–1493. [PMC free article] [PubMed]
  • Carandini M, Ferster D. Membrane Potential and Firing Rate in Cat Primary Visual Cortex. J Neurosci. 2000;20:470–484. [PubMed]
  • Carandini M, Heeger D. Summation and division by neurons in primate visual cortex. Science. 1994;264:1333–1336. [PubMed]
  • Carandini M, Heeger DJ, Movshon JA. Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex. J Neurosci. 1997;17:8621–8644. [PubMed]
  • Carandini M, Heeger DJ, Senn W. A Synaptic Explanation of Suppression in Visual Cortex. J Neurosci. 2002;22:10053–10065. [PubMed]
  • Cardin JA, Palmer LA, Contreras D. Stimulus Feature Selectivity in Excitatory and Inhibitory Neurons in Primary Visual Cortex. J Neurosci. 2007;27:10333–10344. [PMC free article] [PubMed]
  • DeAngelis GC, Robson JG, Ohzawa I, Freeman RD. Organization of suppression in receptive fields of neurons in cat visual cortex. J Neurophysiol. 1992;68:144–163. [PubMed]
  • Debarbieux F, Brunton J, Charpak S. Effect of Bicuculline on Thalamic Activity: A Direct Blockade of IAHP in Reticularis Neurons. J Neurophysiol. 1998;79:2911–2918. [PubMed]
  • Efron B, Tibshirani R. An Introduction to the Bootstrap. Chapman and Hall; London: 1993.
  • Eysel UT, Shevelev IA. Time-slice analysis of inhibition in cat striate cortical neurones. Neuroreport. 1994;5:2033–2036. [PubMed]
  • Ferster D, Miller KD. Neural Mechanisms of Orientation Selectivity in the Visual Cortex. Annu Rev Neurosci. 2000;23:441–471. [PubMed]
  • Finn IM, Priebe NJ, Ferster D. The Emergence of Contrast-Invariant Orientation Tuning in Simple Cells of Cat Visual Cortex. Neuron. 2007;54:137–152. [PMC free article] [PubMed]
  • Freeman TCB, Durand S, Kiper DC, Carandini M. Suppression without Inhibition in Visual Cortex. Neuron. 2002;35:759–771. [PubMed]
  • Haider B, Duque A, Hasenstaub AR, McCormick DA. Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition. J Neurosci. 2006;26:4535–4545. [PubMed]
  • Haider B, Krause MR, Duque A, Yu Y, Touryan J, Mazer JA, McCormick DA. Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation. 2010;65:107–121. [PMC free article] [PubMed]
  • Heeger DJ. Normalization of cell responses in cat striate cortex. Vis Neurosci. 1992;9:181–197. [PubMed]
  • Kleiner M, Brainard DH, Pelli DG. What's new in Psychtoolbox-3? Perception 36 ECVP Abstract Supplement. 2007
  • Kurt S, Crook JM, Ohl FW, Scheich H, Schulze H. Differential effects of iontophoretic in vivo application of the GABAA-antagonists bicuculline and gabazine in sensory cortex. Hearing Research. 2006;212:224–235. [PubMed]
  • Lee J, Maunsell JH. The effect of attention on neuronal responses to high and low contrast stimuli. J Neurophysiol. 2010;104:960–971. [PubMed]
  • Li B, Thompson JK, Duong T, Peterson MR, Freeman RD. Origins of cross-orientation suppression in the visual cortex. J Neurophysiol. 2006;96:1755–1764. [PubMed]
  • Li G, Yang Y, Liang Z, Xia J, Yang Y, Zhou Y. GABA-mediated inhibition correlates with orientation selectivity in primary visual cortex of cat. Neuroscience. 2008;155:914–922. [PubMed]
  • Marino J, Schummers J, Lyon DC, Schwabe L, Beck O, Wiesing P, Obermayer K, Sur M. Invariant computations in local cortical networks with balanced excitation and inhibition. Nat Neurosci. 2005;8:194–201. [PubMed]
  • Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C. Interneurons of the neocortical inhibitory system. Nat Rev Neurosci. 2004;5:793–807. [PubMed]
  • Monier C, Chavane F, Baudot P, Graham LJ, Fregnac Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. Neuron. 2003;37:663–680. [PubMed]
  • Morrone MC, Burr DC, Maffei L. Functional Implications of Cross-Orientation Inhibition of Cortical Visual Cells. I. Neurophysiological Evidence. Proceedings of the Royal Society of London. Series B, Biological Sciences. 1982;216:335–354. [PubMed]
  • Morrone MC, Burr DC, Speed HD. Cross-orientation inhibition in cat is GABA mediated. Exp Brain Res. 1987;67:635–644. [PubMed]
  • Nauhaus I, Ringach DL. Precise Alignment of Micromachined Electrode Arrays With V1 Functional Maps. J Neurophysiol. 2007;97:3781–3789. [PubMed]
  • Nelson S, Toth L, Sheth B, Sur M. Orientation selectivity of cortical neurons during intracellular blockade of inhibition. Science. 1994;265:774–777. [PubMed]
  • Okun M, Lampl I. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci. 2008;11:535–537. [PubMed]
  • Ozeki H, Finn IM, Schaffer ES, Miller KD, Ferster D. Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression. 2009;62:578–592. [PMC free article] [PubMed]
  • Ozeki H, Sadakane O, Akasaki T, Naito T, Shimegi S, Sato H. Relationship between Excitation and Inhibition Underlying Size Tuning and Contextual Response Modulation in the Cat Primary Visual Cortex. J Neurosci. 2004;24:1428–1438. [PubMed]
  • Pettigrew JD, Daniels JD. Gamma-aminobutyric acid antagonism in visual cortex: different effects on simple, complex, and hypercomplex neurons. Science. 1973;182:81–83. [PubMed]
  • Poo C, Isaacson JS. Odor Representations in Olfactory Cortex: Sparse Coding, Global Inhibition, and Oscillations. 2009;62:850–861. [PMC free article] [PubMed]
  • Priebe NJ, Ferster D. Direction selectivity of excitation and inhibition in simple cells of the cat primary visual cortex. Neuron. 2005;45:133–145. [PubMed]
  • Priebe NJ, Ferster D. Mechanisms underlying cross-orientation suppression in cat visual cortex. Nat Neurosci. 2006;9:552–561. [PubMed]
  • Priebe NJ, Ferster D. Inhibition, Spike Threshold, and Stimulus Selectivity in Primary Visual Cortex. Neuron. 2008;57:482–497. [PubMed]
  • Priebe NJ, Mechler F, Carandini M, Ferster D. The contribution of spike threshold to the dichotomy of cortical simple and complex cells. Nat Neurosci. 2004;7:1113–1122. [PMC free article] [PubMed]
  • Ringach DL, Shapley RM, Hawken MJ. Orientation Selectivity in Macaque V1: Diversity and Laminar Dependence. J Neurosci. 2002;22:5639–5651. [PubMed]
  • Rose D, Blakemore C. Effects of bicuculline on functions of inhibition in visual cortex. Nature. 1974;249:375–377. [PubMed]
  • Sato H, Katsuyama N, Tamura H, Hata Y, Tsumoto T. Mechanisms underlying orientation selectivity of neurons in the primary visual cortex of the macaque. The Journal of Physiology. 1996;494:757–771. [PubMed]
  • Sengpiel F, Baddeley RJ, Freeman TC, Harrad R, Blakemore C. Different mechanisms underlie three inhibitory phenomena in cat area 17. Vision Res. 1998;38:2067–2080. [PubMed]
  • Sengpiel F, Blakemore C. Interocular control of neuronal responsiveness in cat visual cortex. Nature. 1994;368:847–850. [PubMed]
  • Sillito AM. The contribution of inhibitory mechanisms to the receptive field properties of neurones in the striate cortex of the cat. The Journal of Physiology. 1975;250:305–329. [PubMed]
  • Sillito AM. Inhibitory mechanisms influencing complex cell orientation selectivity and their modification at high resting discharge levels. Journal of Physiology. 1979;289:33–53. [PubMed]
  • Sillito AM, Kemp JA, Milson JA, Berardi N. A re-evaluation of the mechanisms underlying simple cell orientation selectivity. Brain Research. 1980;194:517–520. [PubMed]
  • Skottun BC, De Valois RL, Grosof DH, Movshon JA, Albrecht DG, Bonds AB. Classifying simple and complex cells on the basis of response modulation. Vision Res. 1991;31:1079–1086. [PubMed]
  • Somers DC, Todorov EV, Siapas AG, Toth LJ, Kim DS, Sur M. A local circuit approach to understanding integration of long-range inputs in primary visual cortex. Cereb Cortex. 1998;8:204–217. [PubMed]
  • Sompolinsky H, Shapley R. New perspectives on the mechanisms for orientation selectivity. Curr Opin Neurobiol. 1997;7:514–522. [PubMed]
  • Thiele A, Distler C, Korbmacher H, Hoffmann KP. Contribution of inhibitory mechanisms to direction selectivity and response normalization in macaque middle temporal area. Proc Natl Acad Sci U S A. 2004;101:9810–9815. [PubMed]
  • Thomson AM, Lamy C. Functional maps of neocortical local circuitry. Front Neurosci. 2007;1:19–42. [PMC free article] [PubMed]
  • Tsumoto T, Eckart W, Creutzfeldt O. Modification of orientation sensitivity of cat visual cortex neurons by removal of GABA-mediated inhibition. Exp Brain Res. 1979;34:351–363. [PubMed]
  • Vidyasagar TR, Pei X, Volgushev M. Multiple mechanisms underlying the orientation selectivity of visual cortical neurones. Trends Neurosci. 1996;19:272–277. [PubMed]
  • Wehr M, Zador AM. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature. 2003;426:442–446. [PubMed]
  • Wermuth CG, Bizière K. Pyridazinyl-GABA derivatives: a new class of synthetic GABAA antagonists. Trends in Pharmacological Sciences. 1986;7:421–424.
  • Williams SR, Mitchell SJ. Direct measurement of somatic voltage clamp errors in central neurons. Nat Neurosci. 2008;11:790–798. [PubMed]